TR2023-046
Task-Directed Exploration in Continuous POMDPs for Robotic Manipulation of Articulated Objects
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- "Task-Directed Exploration in Continuous POMDPs for Robotic Manipulation of Articulated Objects", IEEE International Conference on Robotics and Automation (ICRA), DOI: 10.1109/ICRA48891.2023.10160306, May 2023, pp. 3721-3728.BibTeX TR2023-046 PDF
- @inproceedings{Curtis2023may,
- author = {Curtis, Aidan and Kaelbling, Leslie and Jain, Siddarth},
- title = {Task-Directed Exploration in Continuous POMDPs for Robotic Manipulation of Articulated Objects},
- booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
- year = 2023,
- pages = {3721--3728},
- month = may,
- publisher = {IEEE},
- doi = {10.1109/ICRA48891.2023.10160306},
- isbn = {979-8-3503-2365-8},
- url = {https://www.merl.com/publications/TR2023-046}
- }
,
- "Task-Directed Exploration in Continuous POMDPs for Robotic Manipulation of Articulated Objects", IEEE International Conference on Robotics and Automation (ICRA), DOI: 10.1109/ICRA48891.2023.10160306, May 2023, pp. 3721-3728.
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Research Areas:
Abstract:
Representing and reasoning about uncertainty is crucial for autonomous agents acting in partially observable environments with noisy sensors. Partially observable Markov decision processes (POMDPs) serve as a general framework for representing problems in which uncertainty is an important factor. Online sample-based POMDP methods have emerged as efficient approaches to solving large POMDPs and have been shown to extend to continuous domains. However, these solutions struggle to find long-horizon plans in problems with significant uncertainty. Exploration heuristics can help guide planning, but many real-world settings contain significant task-irrelevant uncertainty that might distract from the task objective. In this paper, we propose STRUG, an online POMDP solver capable of handling domains that require long-horizon planning with significant task-relevant and task-irrelevant uncertainty. We demonstrate our solution on several temporally extended versions of toy POMDP problems as well as robotic manipulation of articulated objects using a neural perception frontend to construct a distribution of possible models. Our results show that STRUG outperforms the current sample- based online POMDP solvers on several tasks.
Related News & Events
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NEWS MERL Researchers Present Thirteen Papers at the 2023 IEEE International Conference on Robotics and Automation (ICRA) Date: May 29, 2023 - June 2, 2023
Where: 2023 IEEE International Conference on Robotics and Automation (ICRA)
MERL Contacts: Anoop Cherian; Radu Corcodel; Siddarth Jain; Devesh K. Jha; Toshiaki Koike-Akino; Tim K. Marks; Daniel N. Nikovski; Arvind Raghunathan; Diego Romeres
Research Areas: Computer Vision, Machine Learning, Optimization, RoboticsBrief- MERL researchers will present thirteen papers, including eight main conference papers and five workshop papers, at the 2023 IEEE International Conference on Robotics and Automation (ICRA) to be held in London, UK from May 29 to June 2. ICRA is one of the largest and most prestigious conferences in the robotics community. The papers cover a broad set of topics in Robotics including estimation, manipulation, vision-based object recognition and segmentation, tactile estimation and tool manipulation, robotic food handling, robot skill learning, and model-based reinforcement learning.
In addition to the paper presentations, MERL robotics researchers will also host an exhibition booth and look forward to discussing our research with visitors.
- MERL researchers will present thirteen papers, including eight main conference papers and five workshop papers, at the 2023 IEEE International Conference on Robotics and Automation (ICRA) to be held in London, UK from May 29 to June 2. ICRA is one of the largest and most prestigious conferences in the robotics community. The papers cover a broad set of topics in Robotics including estimation, manipulation, vision-based object recognition and segmentation, tactile estimation and tool manipulation, robotic food handling, robot skill learning, and model-based reinforcement learning.